Differential abundance analysis using ANCOMBC was carried out on unrarefied data due to the recommendations by the package authors and discussions online. For significant taxa associated with different site-treatment groups, the data was also non-aggregated to ensure there were enough samples for ANCOM analysis.
No genera were identified as differentially abundant when comparing control and water stress samples
## [1] "Control" "Drought"No differentially abundant taxa were identified between control and water stress samples on any of the sites
## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs produced## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs produced## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs produced## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs produced## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs produced## Checking the input data type ...## The input data is of type: phyloseq## PASS## Checking the sample metadata ...## The specified variables in the formula: Treatment## The available variables in the sample metadata: Site, Field_no, Treatment, Group_by## PASS## Checking other arguments ...## PASS## Obtaining initial estimates ...## Estimating sample-specific biases ...## Conducting sensitivity analysis for pseudo-count addition to 0s ...
## For taxa that are significant but do not pass the sensitivity analysis,
## please flag them and proceed with caution, as they are likely false positives.
## For detailed instructions on performing sensitivity analysis,
## please refer to the package vignette.## ANCOM-BC2 primary results ...## Warning in pt(abs(W), df = dof, lower.tail = FALSE): NaNs producedNo differentially abundant families were identified between control and water stress samples on any site
No phyla were identified as differentially abundant when comparing control and water stress samples
No differentially abundant phyla were identified as responding to changes in soil moisture on any site
No differentially abundant genera were identified between control and water stressed samples
No differentially abundant phyla were identified as responding to changes in soil moisture on any site